A tree-based radial basis function method for noisy parallel surrogate optimization

Abstract

Parallel surrogate optimization algorithms have proven to be efficient methods for solving expensive noisy optimization problems. In this work we develop a new parallel surrogate optimization algorithm (ProSRS), using a novel tree-based "zoom strategy" to improve the efficiency of the algorithm. We prove that if ProSRS is run for sufficiently long, with probability converging to one there will be at least one point among all the evaluations that will be arbitrarily close to the global minimum. We compare our algorithm to several state-of-the-art Bayesian optimization algorithms on a suite of standard benchmark functions and two real machine learning hyperparameter-tuning problems. We find that our algorithm not only achieves significantly faster optimization convergence, but is also 1-4 orders of magnitude cheaper in computational cost.

References in corpus (6)

• Practical Bayesian Optimization of Machine Learning Algorithms
• Batch Bayesian Optimization via Local Penalization
• Bayesian Optimization with Gradients
• The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
• Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions
• Simple regret for infinitely many armed bandits